Interpretable Machine Learning Applications: Part 2
By the end of this project, you will be able to develop intepretable machine learning applications explaining individual predictions rather than explaining the behavior of the prediction model as a whole. This will be done via the well known Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation and explanation model. In particular, in this project, you will learn how to go beyond the development and use of machine learning (ML) models, such as regression classifiers, in that we add on explainability and interpretation aspects for individual predictions.
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- Level Beginner
- Duration 2 hours
- Course by Coursera
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Offered by
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Practical Application via Rhyme
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Assignment
- Graded Quiz: Test your knowledge about this guided project
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Labs
- Interpretable Machine Learning Applications: Part 2
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Readings
- Interpretable Machine Learning Applications: Part 2
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Enhance your expertise in Data Science & AI with "Interpretable Machine Learning Applications: Part 2," designed to empower you with the skills to create interpretable machine learning applications. This course focuses on explaining individual predictions using the Local Interpretable Model-agnostic Explanations (LIME) framework, moving beyond traditional model explanations. Led by Coursera, this foundational guided project is ideal for data scientists, machine learning modelers, and executive planners in business or public sectors who aim to leverage machine learning not just as a "black box," but as an insightful tool for decision-making. Over the span of 120 minutes, you will learn to add explainability and interpretation to individual predictions, thus boosting your career in developing trusted, accountable ML applications. Subscribe to the Starter plan to access this engaging and informative project, and take a significant step towards becoming a proficient machine learning professional capable of justifying and explaining model behaviors effectively.
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